Javascript

Configure your project with Drizzle for Local & Deployed Databases

Published Mar 8, 2024

Updated Apr 8, 2024

6 min read

Updated April 8, 2024: Thanks to Jassi Bacha for pointing out that the migrate function is not exported from the drizzle-orm/postgres-js/migrator module for Vercel. I've updated the article to reflect the correct import path.

It was a fun Friday, and Jason Lengstorf and I both decided to try and use Drizzle on our respective projects. Jason went the SQLite route and wrote an amazing article about how he got his setup working. My approach was a bit different. I started with Vercel's Postgres + Drizzle Next.js Starter and wanted to use PostgreSQL. If you don't know what Drizzle is, it's a type-safe ORM similar to Prisma. My colleague, Dane Grant, wrote a great intro post on it, so go check his article out if you want to learn more about Drizzle.

Getting my project off the ground took longer than I expected, especially coming from a starter kit, but I figured it out. This is the article I wish I had at the time to help get this project set up with less friction. I will focus on using local and Vercel PostgreSQL, but this same setup should work with other databases and adapters. I'll make some notes about where those places are. While I did use Next.js here, these setup instructions work on other projects, too.

Configuring Drizzle

Every project that leverages Drizzle requires a drizzle.config in the root. Because I'm leveraging TypeScript, I named mine drizzle.config.ts, and to secure secrets, I also installed dotenv. My final file appeared as follows:

import type { Config } from "drizzle-kit";
import * as dotenv from "dotenv";

dotenv.config();

export default {
  schema: "./db/schema.ts",
  out: "./db/migrations",
  driver: "pg",
  dbCredentials: {
    connectionString: process.env.POSTGRES_URL as string,
  },
} satisfies Config;

The schema field is used to identify where your project's database schema is defined. Mine is in a file called schema.ts but you can split your schema into multiple files and use glob patterns to detect all of them.

The out field determines where your migration outputs will be stored. I recommend putting them in a folder in the same directory as your schema to keep all your database-related information together.

Additionally, the config requires a driver and dbCredentials.connectionString to be specified so Drizzle knows what APIs to leverage and where your database lives. For the connectionString, I'm using dotenv to store the value in a secret and protect it. The connectionString should be in a valid connection format for your database. For PostgreSQL, this format is postgresql://<username>:<password>@<hostname>:<port>/<database_name>.

Getting your connection string

Now, you may be wondering how to get that connection string. If you're hosting on Vercel using their Postgres offering, you need to go to your Vercel dashboard and select their Postgres option and attach it to your app. This will set environment variables for you that you can pull into your local development environment. This is all covered in their "Getting Started with Vercel Postgres" guide. If you use a different database hosting solution, they'll offer similar instructions for fetching your connection string. drizzle screenshot However, I wanted a local database to modify and blow away as needed for this project. Out of the box, Drizzle does not offer a database initialization command and I needed something that could be easily and quickly replicated across different machines. For this, I pulled in Docker Compose and set up my docker-compose.yaml as follows:

services:
  postgres:
    image: "postgres:15.2-alpine"
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: my-local-db
    ports:
      - "5432:5432"

The 3 most important values to note here are the values in the environment key and the ports. These are what allowed me to determine my connection key. For this example, it would be: postgresql://postgres:postgres@localhost:5432/my-local-db. With the compose file set, I ran docker-compose up -d to get the container running, which also initializes the database. Now, we can connect and operate on the database as needed.

Creating the database connection

To make operations in our app, we need to get a database connection instance. I put mine in db/drizzle.ts to keep all my related database files together. My file looks like:

import { sql } from "@vercel/postgres";
import postgres from "postgres";
import {
  drizzle as VercelDrizzle,
  type VercelPgDatabase,
} from "drizzle-orm/vercel-postgres";
import {
  drizzle as LocalDrizzle,
  type PostgresJsDatabase,
} from "drizzle-orm/postgres-js";

let db:
  | VercelPgDatabase<Record<string, never>>
  | PostgresJsDatabase<Record<string, never>>;
if (process.env.NODE_ENV === "production") {
  db = VercelDrizzle(sql);
} else {
  const migrationClient = postgres(process.env.POSTGRES_URL as string);
  db = LocalDrizzle(migrationClient);
}

export { db };

This is a bit more complicated because we're using 2 different Drizzle adapters depending on our environment. For local development, we're using the generic PostgreSQL adapter, but for production, we're using the Vercel adapter. While these have different initializers, they have the same output interface which is why this works. The same wouldn't be true if you used MySQL locally and PostgreSQL in production. If we chose a RDS or similar PostgreSQL solution, we could use the same postgres adapter in both cases but change the connection string. That's all this file does at the end - detects which environment and uses the chosen adapter.

If we go to use this exported instance, it won't be able to find our tables or provide type safety. This is because we haven't created our database tables yet.

Creating database tables

To get our database tables created, we're going to leverage Drizzle's Migrations. This allows us to create atomic changes to our database as our schema evolves. To accomplish this, we define the schema changes in our schema files as specified in our config. Then we can run npm run drizzle-kit generate:pg (or whatever script runner you use) to generate the migration SQL file that will be located where we specified in our config. You want to check this file into source! By default, Drizzle doesn't allow you to override migration names yet (they're working on it!) so if you want to make your migration file more descriptive, you need to take both of these steps:

Rename the migration file. Take note of the old name.
Locate _journal.json. It should be in your migration folder in a folder called meta. From here, find the old file name and replace it with the new file name.

Now, we need to run the migrations. I had some issues with top-level awaits and tsx like the Drizzle docs recommend, so I had to go a slightly different route and I'm not thrilled about it still. I made a file called migrate.mts that I stored next to my drizzle.ts. In theory, I should have been able to import my drizzle connection instance here and use that, but I ran out of time to figure it out and ended up repeating myself across files. Here's the file:

import "dotenv/config";
import { migrate as LocalMigrate } from "drizzle-orm/postgres-js/migrator";
import { migrate as VercelMigrate } from "drizzle-orm/vercel-postgres/migrator";
import postgres from "postgres";
import { sql } from "@vercel/postgres";
import { drizzle as VercelDrizzle } from "drizzle-orm/vercel-postgres";
import { drizzle as LocalDrizzle } from "drizzle-orm/postgres-js";

// Could not import from drizzle.ts due to mts v ts compatibility issues
let db;
if (process.env.NODE_ENV === "production") {
  db = VercelDrizzle(sql);
  await VercelMigrate(db, { migrationsFolder: "./db/migrations" });
} else {
  const migrationClient = postgres(process.env.POSTGRES_URL as string, {
    max: 1,
  });
  db = LocalDrizzle(migrationClient);
  await LocalMigrate(db, { migrationsFolder: "./db/migrations" });
  await migrationClient.end();
}

Here, I'm connecting to the correct database pending environment then running the drizzle migrate command. For local development, I set my connection pool to max at 1. This probably isn't necessary for this use case, but when connecting to a cluster, this is a recommended best practice from the Drizzle team. For the local case, I also had to close the connection to the db when I was done. For both cases, though, I had to specify the migrations folder location. I could probably DRY this up a bit, but hopefully, the Drizzle team will eliminate this need and use the config to set this value in the future.

With the above file set and our schema generated, we can now run npm run tsx db/migrate.mts and our database will have our latest schema. We can now use the db client to fetch and store data in our database.

Note: Jason uses the push command here. This is fine for an initial database creation, but it will override tables in the future. The migration path is the recommended pattern for non-destructive database updates.

Conclusion

Congratulations! We can connect to our database and perform CRUD operations against our tables. We can also use Drizzle Studio to modify and inspect our data. To review, we had to:

Setup a local PostgreSQL server via a tool like Docker Compose
Configure the database adapter to work in local mode
Generate a schema
Create a script to execute migrations so our database is aligned with our schema

This was my first experience with Drizzle, and I enjoyed its SQL-like interfaces which made it easy to quickly prototype my project. I noticed in their Discord that they're about to have a full-time maintainer so I'm excited to see what their future looks like. I hope you enjoy it too!

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About the author(s)

Dustin Goodman
Engineering Manager with a passion for web and application development. Speaker and writer on work experiences and software development. Dog dad and musician fueled by coffee.
@dustinsgoodman @dustinsgoodman

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Danny Thompson, Director of Technology at This Dot Labs, talks with Tanner Linsley, Creator of TanStack, about his latest project, TanStack Start. They discuss the challenges of existing frameworks like Next.js and Remix, the development of TanStack Router, and the future of React Server Components. Tanner also explains how caching strategies and fine-grained invalidation can transform the user experience. Chapters - - Introduction & Tanner’s Background (00:00) - Going Full-Time on TanStack (01:00) - The Birth of TanStack Router (02:21) - Why Build Another Framework? (04:00) - React Server Components: Potential & Limitations (07:05) - Fine-Grained Cache Invalidation & UX (09:02) - Parallel Data Fetching in Routing (13:39) - TanStack Start: Alpha & Future Plans (16:41) - Where to Learn More About TanStack (18:48) Find Tanner Linsley on Social Media Twitter: https://x.com/tannerlinsley Linkedin: https://www.linkedin.com/in/tannerlinsley/ Github: https://github.com/tannerlinsley TanStack: https://tanstack.com/ Sponsored by Wix Studio...

Oct 9, 2024

1 min

TanStack QueryJavaScriptReact

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

The HTML Dialog Element: Enhancing Accessibility and Ease of Use Dialogs are a common component added to applications, whether on the web or in native applications. Traditionally there has not been a standard way of implementing these on the web, resulting in many ad-hoc implementations that don’t act consistently across different web applications. Often, commonly expected features are missing from dialogs due to the complexity of implementing them. However, web browsers now offer a standard dialog element. Why use the dialog element? The native dialog element streamlines the implementation of dialogs, modals, and other kinds of non-modal dialogs. It does this by implementing many of the features needed by dialogs for you that are already baked into the browser. This is helpful as it reduces the burden on the developer when making their applications accessible by ensuring that user expectations concerning interaction are met, and it can also potentially simplify the implementation of dialogs in general. Basic usage Adding a dialog using the new tag can be achieved with just a few lines of code. ` However, adding the dialog alone won’t do anything to the page. It will show up only once you call the .showModal() method against it. ` Then if you want to close it you can call the .close() method on the dialog, or press the escape key to close it, just like most other modals work. Also, note how a backdrop appears that darkens the rest of the page and prevents you from interacting with it. Neat! Accessibility and focus management Correctly handling focus is important when making your web applications accessible to all users. Typically you have to move the current focus to the active dialog when showing them, but with the dialog element that’s done for you. By default, the focus will be set on the first focusable element in the dialog. You can optionally change which element receives focus first by setting the autofocus attribute on the element you want the focus to start on, as seen in the previous example where that attribute was added to the close element. Using the .showModal() method to open the dialog also implicitly adds the dialog ARIA role to the dialog element. This helps screen readers understand that a modal has appeared and the screen so it can act accordingly. Adding forms to dialogs Forms can also be added to dialogs, and there’s even a special method value for them. If you add a element with the method set to dialog then the form will have some different behaviors that differ from the standard get and post form methods. First off, no external HTTP request will be made with this new method. What will happen instead is that when the form gets submitted, the returnValue property on the form element will be set to the value of the submit button in the form. So given this example form: ` The form element with the example-form id will have its returnValue set to Submit. In addition to that, the dialog will close immediately after the submit event is done being handled, though not before automatic form validation is done. If this fails then the invalid event will be emitted. You may have already noticed one caveat to all of this. You might not want the form to close automatically when the submit handler is done running. If you perform an asynchronous request with an API or server you may want to wait for a response and show any errors that occur before dismissing the dialog. In this case, you can call event.preventDefault() in the submit event listener like so: ` Once your desired response comes back from the server, you can close it manually by using the .close() method on the dialog. Enhancing the backdrop The backdrop behind the dialog is a mostly translucent gray background by default. However, that backdrop is fully customizable using the ::backdrop pseudo-element. With it, you can set a background-color to any value you want, including gradients, images, etc. You may also want to make clicking the backdrop dismiss the modal, as this is a commonly implemented feature of them. By default, the <dialog> element doesn’t do this for us. There are a couple of changes that we can make to the dialog to get this working. First, an event listener is needed so that we know when the user clicks away from the dialog. ` Alone this event listener looks strange. It appears to dismiss the dialog whenever the dialog is clicked, not the backdrop. That’s the opposite of what we want to do. Unfortunately, you cannot listen for a click event on the backdrop as it is considered to be part of the dialog itself. Adding this event listener by itself will effectively make clicking anywhere on the page dismiss the dialog. To correct for this we need to wrap the contents of the dialog content with another element that will effectively mask the dialog and receive the click instead. A simple element can do! ` Even this isn’t perfect though as the contents of the div may have elements with margins in them that will push the div down, resulting in clicks close to the edges of the dialog to dismiss it. This can be resolved by adding a couple of styles the the wrapping div that will make the margin stay contained within the wrapper element. The dialog element itself also has some default padding that will exacerbate this issue. ` The wrapping div can be made into an inline-block element to contain the margin, and by moving the padding from the parent dialog to the wrapper, clicks made in the padded portions of the dialog will now interact with the wrapper element instead ensuring it won’t be dismissed. Conclusion Using the dialog element offers significant advantages for creating dialogs and modals by simplifying implementation with reasonable default behavior, enhancing accessibility for users that need assistive technologies such as screen readers by using automatic ARIA role assignment, tailored support for form elements, and flexible styling options....

Oct 25, 2024

4 mins

HTMLJavaScript

How to Leverage Apollo Client Fetch Policies Like the Pros

Apollo Client provides a rich ecosystem and cache for interfacing with your GraphQL APIs. You write your query and leverage the useQuery hook to fetch your data. It provides you with some state context and eventually resolves your query. That data is stored in a local, normalized, in-memory cache, which allows Apollo Client to respond to most previously run requests near instantaneously. This has huge benefits for client performance and the feel of your apps. However, sometimes Apollo's default doesn't match the user experience you want to provide. They provide fetch policies to allow you to control this behavior on each query you execute. In this article, we'll explore the different fetch policies and how you should leverage them in your application. cache-first This is the default for Apollo Client. Apollo will execute your query against the cache. If the cache can fully fulfill the request, then that's it, and we return to the client. If it can only partially match your request or cannot find any of the related data, the query will be run against your GraphQL server. The response is cached for the next query and returned to the handler. This method prioritizes minimizing the number of requests sent to your server. However, it has an adverse effect on data that changes regularly. Think of your social media feeds - they typically contain constantly changing information as new posts are generated. Or a real-time dashboard app tracking data as it moves through a system. cache-first is probably not the best policy for these use cases as you won't fetch the latest data from the upstream source. You can lower the cache time of items for the dashboard to avoid the staleness issue and still minimize the requests being made, but this problem will persist for social media feeds. The cache-first policy should be considered for data that does not change often in your system or data that the current user fully controls. Data that doesn't change often is easily cached, and that's a recommended pattern. For data that the user controls, we need to consider how that data changes. If only the current user can change it, we have 2 options: Return the updated data in the response of any mutation which is used to update the cache Use cache invalidation methods like refetchQueries or onQueryUpdated These methods will ensure that our cache stays in sync with our server allowing the policy to work optimally. However, if other users in the system can make changes that impact the current user's view, then we can not invalidate the cache properly using these strategies which makes this policy unideal. network-only This policy skips the cache lookup and goes to the server to fetch the results. The results are stored in the cache for other operations to leverage. Going back to the example I gave in my explanation cache-first of a social media feed, the network-only policy would be a great way to implement the feed itself as it's ever-changing, and we'll likely even want to poll for changes every 10s or so. The following is an example of what this component could look like: ` Whenever this SocialFeed component is rendered, we always fetch the latest results from the GraphQL server ensuring we're looking at the current data. The results are put in the cache which we can leverage in some children components. cache-only cache-only only checks the cache for the requested data and never hits the server. It throws an error if the specified cache items cannot be found. At first glance, this cache policy may seem unhelpful because it's unclear if our cache is seeded with our data. However, in combination with the network-only policy above, this policy becomes helpful. This policy is meant for components down tree from network-only level query. This method is for you if you're a fan of React components' compatibility. We can modify the return of our previous example to be as follows: ` Notice we're not passing the full post object as a prop. This simplifies our Post component types and makes later refactors easier. The Post would like like the following: ` In this query, we're grabbing the data directly from our cache every time because our top-level query should have fetched it. Now, a small bug here makes maintainability a bit harder. Our top-level GetFeed query doesn't guarantee fetching the same fields. Notice how our Post component exports a fragment. Fragments are a feature Apollo supports to share query elements across operations. In our SocialFeed component, we can change our query to be: ` Now, as we change our Post to use new fields and display different data, the refactoring is restricted to just that component, and the upstream components will detect the changes and handle them for us making our codebase more maintainable. Because the upstream component is always fetching from the network, we can trust that the cache will have our data, making this component safe to render. With these examples, though, our users will likely have to see a loading spinner or state on every render unless we add some server rendering. cache-and-network This is where cache-and-network comes to play. With this policy, Apollo Client will run your query against your cache and your GraphQL server. This further simplifies our example above if we want to provide the last fetched results to the user but then update the feed immediately upon gathering the latest data. This is similar to what X/Twitter does when you reload the app. You'll see the last value that was in the cache then it'll render the network values when ready. This can cause a jarring user experience though, if the data is changing a lot over time, so I recommend using this methodology sparsely. However, if you wanted to update our existing example, we'd just change our SocialFeed component to use this policy, and that'll keep our client and server better in sync while still enabling 10s polling. no-cache This policy is very similar to the network-only policy, except it bypasses the local cache entirely. In our previous example, we wrote engagement as a sub-selector on a Post and stored fields there. These metrics can change in real time pretty drastically. Chat features, reactions, viewership numbers, etc., are all types of data that may change in real time. The no-cache policy is good when this type of data is active, such as during a live stream or within the first few hours of a post going out. You may typically want to use the cache-and-network policy eventually but during that active period, you'll probably want to use no-cache so your consumers can trust your data. I'd probably recommend changing your server to split these queries and run different policies for the operations for performance reasons. I haven't mentioned this yet, but you can make the fetch policy on a query dynamic, meaning you combine these different policies' pending states. This could look like the following: ` We pass whether the event is live to the component that then leverages that info to determine if we should cache or not when fetching the chat. That being said, we should consider using subscription operations for this type of feature as well, but that's an exercise for another blog post. standby This is the most uncommon fetch policy, but has a lot of use. This option runs like a cache-first query when it executes. However, by default, this query does not run and is treated like a "skip" until it is manually triggered by a refetch or updateQueries caller. You can achieve similar results by leveraging the useLazyQuery operator, but this maintains the same behavior as other useQuery operators so you'll have more consistency among your components. This method is primarily used for operations pending other queries to finish or when you want to trigger the caller on a mutation. Think about a dashboard with many filters that need to be applied before your query executes. The standby fetch policy can wait until the user hits the Apply or Submit button to execute the operation then calls a await client.refetchQueries({ include: ["DashboardQuery"] }), which will then allow your component to pull in the parameters for your operation and execute it. Again, you could achieve this with useLazyQuery so it's really up to you and your team how you want to approach this problem. To avoid learning 2 ways, though, I recommend picking just one path. Conclusion Apollo Client's fetch policies are a versatile and helpful tool for managing your application data and keeping it in sync with your GraphQL server. In general, you should use the defaults provided by the library, but think about the user experience you want to provide. This will help you determine which policy best meets your needs. Leveraging tools like fragments will enable you to manage your application and use composable patterns more effectively. With the rise of React Server Components and other similar patterns, you'll need to be wary of how that impacts your Apollo Client strategy. However, if you're on a legacy application that leverages traditional SSR patterns, Apollo allows you to pre-render queries on the server and their related cache. When you combine these technologies, you'll find that your apps perform great, and your users will be delighted....

May 17, 2024

7 mins

GraphQL

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

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Configure your project with Drizzle for Local & Deployed Databases

Configuring Drizzle

Getting your connection string

Creating the database connection

Creating database tables

Conclusion

Dustin Goodman

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Let's innovate together!

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